markus41 Post-Launch Review
Conduct a structured post-go-live assessment of a completed Lobbi automation engagement. The review validates that the automation is delivering its promised outcomes, captures what went well and what to improve, and identifies opportunities for a Phase 2 expansion conversation.
Timing and Setup
Recommended timing: 30–60 days post-go-live. Early enough that memory is fresh; late enough that initial launch anomalies have settled and meaningful production data exists.
Meeting format: 60-minute video call with client. Participants: client project sponsor, client operational SME (person using the automation daily), Lobbi PM, Lobbi account lead.
Pre-meeting data collection: Gather the metrics below before the meeting so the conversation is data-driven, not anecdotal.
Section 1: Outcome Validation
Compare actual performance against the projections from the original ROI calculator.
Processing Metrics
Pull from system logs, AMS/LOS reports, or monitoring dashboards:
| Metric | Projected | Actual | Variance | Notes |
|---|---|---|---|---|
| Daily transaction volume | [N] | [N] | [+/-X%] | |
| Straight-through processing rate | [X]% | [X]% | [+/-X pts] | |
| Average processing time per transaction | [N] min | [N] min | [+/-X%] | |
| Error rate | [X]% | [X]% | [+/-X pts] | |
| Exception rate (manual review required) | [X]% | [X]% | [+/-X pts] | |
| System uptime / availability | ≥99% | [X]% |
Financial Outcomes
| Metric | Projected (Year 1 annualized) | Actual (30/60-day run rate × 12) | Variance |
|---|---|---|---|
| Annual hours recovered | [N] hours | [N] hours | [+/-X%] |
| Annual labor savings | $[X] | $[X] | [+/-X%] |
| Annual error cost reduction | $[X] | $[X] | [+/-X%] |
| Total annual savings | $[X] | $[X] | [+/-X%] |
Variance explanation: For any metric where actual performance is more than 10% below projection, document the root cause:
- Volume lower than expected (business seasonality? adoption lag?)
- Straight-through rate lower than expected (exception categories not anticipated?)
- Error rate higher than expected (data quality issues? edge cases not covered?)
User Adoption
- Number of users trained: [N]
- Number of users actively using the automation: [N] ([X]% adoption rate)
- Transactions still processed manually (should be zero for in-scope workflows): [N]
- If adoption < 100%: identify the users/teams not using the automation and the reason
Section 2: System Health Check
Assess whether the automation is operating reliably in production.
| Health Indicator | Status | Notes |
|---|---|---|
| Integration error rate (external API calls) | [Green <1% / Amber 1-5% / Red >5%] | |
| Processing volume vs. capacity (% of limit) | [Green <70% / Amber 70-85% / Red >85%] | |
| SLA compliance (processing within defined time window) | [Green >99% / Amber 95-99% / Red <95%] | |
| Exception queue backlog | [Green = 0 / Amber = 1-5 / Red = 5+] | |
| Failed notification / communication count | [N] since go-live | |
| Manual overrides or workarounds in use | [Y/N — list if Y] | |
| Any unplanned outages since go-live | [N incidents, total downtime] |
Outstanding issues (known bugs or limitations identified in production):
| Issue | Severity | Workaround in use | Resolution status |
|---|---|---|---|
| [Issue description] | Critical / High / Medium / Low | [Yes / No — describe] | [Scheduled fix date / Backlogged] |
Section 3: User Feedback Collection
Gather structured feedback from end users and managers. Conduct either as a survey (sent before the review meeting) or as a facilitated discussion during the meeting.
For end users (people who interact with the automation daily):
- On a scale of 1–5, how satisfied are you with the automation? [Average score]
- What is the biggest improvement compared to the old manual process?
- What is the most frustrating thing about the current automation?
- Are there situations where you still do this process manually? If yes, why?
- Is there anything you expected the automation to do that it doesn't?
For managers / team leads:
- Has the automation delivered the time savings you expected?
- Have there been any operational issues caused by the automation?
- How has the automation affected team capacity? (New tasks taken on? Headcount impact?)
- How has customer or partner response to the new process been?
- What would make the biggest difference in Phase 2?
Summary of feedback themes:
- Positive themes: [List the 2–3 things users are happiest about]
- Negative themes: [List the 2–3 most common complaints or frustrations]
- Unmet expectations: [List any expectations not met by the automation]
Section 4: Lessons Learned
Capture what went well and what to do differently — for Lobbi's internal improvement, not for client delivery.
What went well:
| Category | Observation |
|---|---|
| Discovery / scoping | [e.g., "Detailed ROI data collected upfront made the proposal compelling and set realistic expectations"] |
| Technical | [e.g., "Applied EPIC API performed reliably — no rate limiting issues"] |
| Delivery | [e.g., "Client UAT testers were well-prepared because of the test script we provided"] |
| Client relationship | [e.g., "Weekly status reports kept the sponsor informed and reduced ad-hoc check-in requests"] |
What to improve:
| Category | Issue | Root Cause | Change for Next Project |
|---|---|---|---|
| Discovery | [e.g., "Volume projections were 30% high — client estimated, didn't measure"] | Client estimated from memory | Ask for 90-day historical data, not estimates |
| Technical | [e.g., "Carrier portal API changed without notice mid-build"] | No version lock / change notification | Add API versioning check to integration checklist |
| Delivery | [e.g., "UAT started 5 days late due to client unavailability"] | UAT timeline not confirmed at kickoff | Lock UAT dates and testers at project kickoff |
What surprised us:
| Surprise | Impact | How we handled it |
|---|---|---|
| [e.g., "3x more exception types than anticipated"] | [+1 week to build] | [Change order / absorbed] |
Section 5: Optimization Opportunities
Identify improvements that fall within the existing system — quick wins that improve performance without a new engagement.
Quick wins (no change order required, configurable changes):
| Opportunity | Current State | Improved State | Effort | Owner |
|---|---|---|---|---|
| [e.g., "Add 2 new exception category auto-resolutions"] | Manual review | Auto-resolved | Low | Lobbi |
| [e.g., "Increase notification email personalization"] | Generic template | Personalized | Low | Lobbi |
Tune-ups (minor change order, < $2K):
| Opportunity | Business Benefit | Estimated Investment |
|---|---|---|
| [Improvement] | [Quantified benefit] | $[X] |
Section 6: Phase 2 Expansion Roadmap
Based on the review, identify the most compelling opportunities for a Phase 2 engagement. These are new automations or significant extensions — not quick wins.
Expansion opportunities (prioritized by client interest and ROI potential):
| Priority | Opportunity | Current State | Proposed Automation | Estimated ROI | Client Interest |
|---|---|---|---|---|---|
| 1 | [e.g., "Policy renewal outreach sequence"] | Manual renewal calls | Automated 90/60/30-day sequence | $[X]/year | High / Medium / Low |
| 2 | |||||
| 3 |
Recommended next step:
Based on this review, we recommend scheduling a 60-minute discovery call focused on [top Phase 2 opportunity]. This would build directly on the current automation and [rationale for why it's the right next step].
Output Format
Produce a Post-Launch Review Report structured as:
- Executive Summary — 3–5 bullet points covering: outcomes vs. projections, system health, user adoption, top lesson, top Phase 2 recommendation
- Outcome Validation — Tables from Section 1
- System Health — Table from Section 2 with green/amber/red summary
- User Feedback Summary — Themes from Section 3
- Lessons Learned — Internal retrospective from Section 4
- Optimization Opportunities — Quick wins from Section 5
- Phase 2 Roadmap — Expansion opportunities from Section 6
The report is suitable for sharing with the client sponsor as a professional engagement closeout document.